Today, sensing resources play a crucial role in the success of critical tasks such as border monitoring and surveillance. Although there are various types of resources available, each with different capabilities, only a subset of these resources are useful for a specific task. This is due to the dynamism in tasks' environment and the heterogeneity of the resources. Thus, an effective mechanism to select resources for tasks is needed so that the selected resources cater for the needs of the tasks. Though a considerable amount of research has already been done in different communities to efficiently allocate resources to tasks, we argue that there is little work done to guarantee the effectiveness of the section with respect to the context of operation. In this thesis, we propose a knowledge-based approach in which the context of operation is introduced to the resource selection process. First, we present a formalism to represent a sensor domain. We then introduce sound and complete mechanisms through which effective resource solutions for tasks are discovered. An extension to the representation is then proposed so that the agility in resource selection is increased. Finally, we present an architecture whereby a multitude of such knowledge bases are exposed as services so that a coalition can fully benefit from its networked resources; a query language – and its semantics – to discover appropriate service collections for user requirements are also presented. We have evaluated our work through controlled experiments and critical arguments. Through these evaluations, we have shown that our approach can indeed improve the resource selection process and can augment resource allocation mechanisms. Our approach is general in that, it can be applied in many other domains.

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In this research, a wind feedforward (FF) controller has been developed to augment closed loop feedback controllers for the position and heading station keeping control of Unmanned Surface Vehicles (USVs). The performance of the controllers was experimentally tested using a 16 foot USV in an outdoor marine environment. The FF controller was combined with three nonlinear feedback controllers, a Proportional–Derivative (PD) controller, a Backstepping (BS) controller, and a Sliding mode (SM) controller, to improve the station-keeping performance of the USV. To address the problem of wind model uncertainties, adaptive wind feedforward (AFF) control schemes are also applied to the FF controller, and implemented together with the BS and SM feedback controllers. The adaptive law is derived using Lyapunov Theory to ensure stability. On-water station keeping tests of each combination of FF and feedback controllers were conducted in the U.S. Intracoastal Waterway in Dania Beach, FL USA. Five runs of each test condition were performed; each run lasted at least 10 minutes. The experiments were conducted in Sea State 1 with an average wind speed of between 1 to 4 meters per second and significant wave heights of less than 0.2 meters. When the performance of the controllers is compared using the Integral of the Absolute Error (IAE) of position criterion, the experimental results indicate that the BS and SM feedback controllers significantly outperform the PD feedback controller (e.g. a 33% and a 44% decreases in the IAE, respectively). It is also found that FF is beneficial for all three feedback controllers and that AFF can further improve the station keeping performance. For example, a BS feedback control combined with AFF control reduces the IAE by 25% when compared with a BS feedback controller combined with a non-adaptive FF controller. Among the eight combinations of controllers tested, SM feedback control combined with AFF control gives the best station keeping performance with an average position and heading error of 0.32 meters and 4.76 degrees, respectively. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Wireless sensor networks (WSNs) have gained attention in recent years with the proliferation of the micro-electro-mechanical systems, which has led to the development of smart sensors. Smart sensors has brought WSNs under the spotlight and has created numerous different areas of research such as; energy consumption, convergence, network structures, deployment methods, time delay, and communication protocols. Convergence rates associated with information propagations of the networks will be questioned in this thesis. Mobility is an expensive process in terms of the associated energy costs. In a sensor network, mobility has significant overhead in terms of closing old connections and creating new connections as mobile sensor nodes move from one location to another. Despite these drawbacks, mobility helps a sensor network reach an agreement more quickly. Adding few mobile nodes to an otherwise static network will significantly improve the network’s ability to reach consensus. This paper shows the effect of the mobility on convergence rate of the wireless sensor networks, through Eigenvalue analysis, modeling and simulation.

Cette thèse s’inscrit dans le cadre de la disposition CIFRE entre la société Robert Bosch et l’Université de Caen Normandie au sein du laboratoire GREYC puis LAC. Elle comprend deux volets, l'un étant à caractère fondamental et porte sur la synthèse d'observateurs. Quant à l'autre volet, il est à caractère plus appliqué et porte sur la mise en œuvre d’un réseau de capteurs et assurer l’acheminement des informations provenant de ces derniers. Ce dernier est effectué au moyen du réseau, qu’il soit câblé ou sans fil.Dans le premier volet de l'étude, on s'est intéressé à une classe assez générale de systèmes temps-variant et affines en l'état, dont la sortie est mesurée avec un retard et échantillonnée. La nouveauté dans cette classe de systèmes est double :(i) l'équation d'état est sujette à une injection du signal de sortie et se retrouve de ce fait dépendante des sorties futures qui sont indisponibles;(ii) les sorties futures interviennent, au niveau de l'équation d'état, non seulement sous la forme usuelle d'une fonction indépendante de l'état, mais aussi à travers la matrice d'état ellemême qui, de ce fait, apparaît comme une quantité inconnue du modèle.Ces deux nouveautés du modèle entrainent en fait la perte du caractère « affine en l'état » de ce dernier et font que l'on se retrouve confronté à un problème de synthèse d'observateurs jamais résolu antérieurement. La solution que nous proposons est un observateur de type « Filtre de Kalman » augmenté d'un prédicteur inter-échantillons et d'opérateurs de saturations. Nous analysons la stabilité exponentielle du système d'erreur d'estimation d'état en utilisant le théorème du petit gain et des outils de la stabilité de Lyapunov. L'analyse met en évidence l'existence d'intervalles d'admissibilité dans lesquels doivent se situer les valeurs admissibles du retard et de la période d'échantillonnage, afin de garantir la convergence exponentielle de l'observateur.Dans le deuxième volet, nous nous intéressons à la maintenance prédictive au travers d’applications pratiques via la mise en place d’un réseau de capteurs. Le but de ce réseau est d’effectuer de la maintenance prédictive sur les équipements sensibles. Ce dernier est un composant essentiel à la mise en œuvre d’applications IoT et Industrie 4.0.Des applications de l’IoT et de l’Industrie 4.0 sur le site Robert Bosch de Mondeville sont évoqués ainsi que le développement d’un simulateur de perturbations réseau afin de tester la robustesse de la communication d’un capteur vers un client. / This thesis is part of the CIFRE agreement between the company Robert Bosch and the University of Caen Normandy in the laboratory GREYC then LAC. It consists of two parts, one which is of a fundamental nature and concerns the synthesis of observers. For the other part, it is more applied and concerns the implementation of a sensors network and ensure the routing of information from them. This is done through the network, whether wired or wireless.In the first part of the study, we looked at a fairly general class of time-varying and affine systems as they are, whose output is measured with a delay and sampled. The novelty in this class of systems is twofold:(i) the state equation is subject to an output signal injection and is therefore dependent on future outputs that are unavailable;(ii) future outputs occur at the state equation not only in the usual form of a stateindependent function, but also through the state matrix itself, which fact, appears as an unknown quantity of the model.These two novelties of the model cause in fact the loss of the "affine in the state" character of this last one, and make that one is confronted with a synthesis problem of observers never resolved previously. The solution we propose is a "Kalman filter" type observer augmented by an inter-sample predictor and saturation operators. We analyze the exponential stability of the state estimation error system by using the small gain theorem and tools of Lyapunov stability. The analysis highlights the existence of eligibility intervals in which the allowable values of the delay and the sampling period must be located in order to ensure the exponential convergence of the observer.In the second part, we are interested in predictive maintenance through practical applications via the installation of a sensor network. The purpose of this network is to perform predictive maintenance on sensitive equipment. The latter is an essential component for the implementation of IoT and Industry 4.0 applications.The purpose of this sensor network is to perform predictive maintenance on sensitive equipment. The latter is an essential component for the implementation of IoT and Industry 4.0 applications. Moreover, an observer of sampled data for affine systems in the state with output injection was studied on the basis of observers.Applications of IoT and Industry 4.0 on the Robert Bosch site in Mondeville are discussed as well as the development of a network disturbance simulator to stress the robustness of the communication of a sensor to a client.

This thesis describes research on methods for Ubiquitous/Pervasive Computing to better suit users in an Intelligent Environment. The approach is to create and equip a computing environment, such as our Active Office, with technologies that can identify user needs and meet these need in a timely, efficient and unobtrusive manner.¶ The critical issues in the Intelligent Environment are how to enable transparent, distributed computing to allow continued operation across changing circumstances and how to exploit the changing environment so that it is aware of the context of user location, the collection of nearby people and objects, accessible devices and changes to those objects over time.¶ Since the Intelligent Environment is an environment with rapid and rich computing processing, the distributed context processing architecture (DiCPA) was developed to manage and respond to rapidly changing aggregation of sensor data. This architecture is a scalable distributed context processing architecture that provides: 1. continued operation across changing circumstances for users, 2. the collection of nearby people and objects, 3. accessible devices and 4. the changes to those objects over time in the environment. The DiCPA approach focuses on how the Intelligent Environment provides context information for user location, user mobility and the user activity model. Users are assumed mobile within the Intelligent Environment and can rapidly change their access to relevant information and the availability of communications and computational resources.¶ Context-Aware Computing is a new approach in software engineering for Intelligent Environment. It is an approach in the design and construction of a context-aware application that exploits rapid changes in access to relevant information and the availability of communication and computing resources in the mobile computing environment. The goal of Context-Aware Computing is to make user interaction with the computer easier in the smart environment where technology is spread throughout (pervasive), computers are everywhere at the same time (ubiquitous) and technology is embedded (ambient) in the environment. Context-aware applications need not be difficult, tedious or require the acquisition of new skills on the part of the user. They should be safe, easy, simple to use and should enable new functionality without the need to learn new technology. They should provide relevant information and a simple way for a user to manage.¶ The Intelligent Environment requires a context-aware application to improve its efficiency and to increase productivity and enjoyment for the user. The context awareness mechanism has four fundamental cores i.e. identity (who), activity (what), location (where) and timestamp (when). Based on DiCPA architecture, the model of user location (where), user mobility (where), user activity (what) and Intelligent Environment response (what) were developed. Prototypes were also developed to proof the Context-Aware Computing concept in the Intelligent Environment.¶ An Intelligent Environment uses the multi-disciplinary area of Context-Aware Computing, which combines technology, computer systems, models and reasoning, social aspects, and user support. A “good quality” project for Context-Aware Computing requires core content and provides iterative evaluation processes, which has two types of iteration: design and product iteration of the evaluation. The aim of the development of an evaluation program in Context-Aware Computing is to determine what to test, how to test and the appropriate metrics to use. This work presents the metrics for a good quality project in the Context-Aware Computing area, which is followed by the evaluation of the prototypes of this work.

Food availability and food waste are signi cant global problems which can be mitigated through the use of sensor networks. Current methods of monitoring food waste require manual data collection and are implemented infrequently, providing imprecise information. The use of sensors to automate food waste measurement allows constant monitoring, provides a better dataset for analysis, and enables real- time feedback, which can be used to affect behavioral change in consumers. The data from such networks can be used to drive ambient displays designed to educate a target audience, and ultimately reduce the amount of waste generated. We present WASTE REDUCE, a system for automating the measurement of food waste and affecting behavioral change. The challenges and results of deploying such a system are presented. To assess the bene ts of using WASTE REDUCE, two case studies are conducted. The rst study evaluates three different displays, and the second reevaluates one of these displays in a separate location. These studies con rm that the combination of automated monitoring and ambient feedback can reduce food waste for targeted groups. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

In smart item environments, multitude of sensors are applied to capture data about product conditions and usage to guide business decisions as well as production automation processes. A big issue in this application area is posed by the restricted quality of sensor data due to limited sensor precision as well as sensor failures and malfunctions. Decisions derived on incorrect or misleading sensor data are likely to be faulty. The issue of how to efficiently provide applications with information about data quality (DQ) is still an open research problem. In this paper, we present a flexible model for the efficient transfer and management of data quality for streaming as well as static data. We propose a data stream metamodel to allow for the propagation of data quality from the sensors up to the respective business application without a significant overhead of data. Furthermore, we present the extension of the traditional RDBMS metamodel to permit the persistent storage of data quality information in a relational database. Finally, we demonstrate a data quality metadata mapping to close the gap between the streaming environment and the target database. Our solution maintains a flexible number of DQ dimensions and supports applications directly consuming streaming data or processing data filed in a persistent database.

A deep scientific understanding of complex physical systems, such as the atmosphere, can be achieved neither by direct measurements nor by numerical simulations alone. Data assimilation is a rigorous procedure to fuse information from a priori knowledge of the system state, the physical laws governing the evolution of the system, and real measurements, all with associated error statistics. Data assimilation produces best (a posteriori) estimates of model states and parameter values, and results in considerably improved computer simulations. The acquisition and use of observations in data assimilation raises several important scientific questions related to optimal sensor network design, quantification of data impact, pruning redundant data, and identifying the most beneficial additional observations. These questions originate in operational data assimilation practice, and have started to attract considerable interest in the recent past. This dissertation advances the state of knowledge in four dimensional variational (4D-Var) - data assimilation by developing, implementing, and validating a novel computational framework for estimating observation impact and for optimizing sensor networks. The framework builds on the powerful methodologies of second-order adjoint modeling and the 4D-Var sensitivity equations. Efficient computational approaches for quantifying the observation impact include matrix free linear algebra algorithms and low-rank approximations of the sensitivities to observations. The sensor network configuration problem is formulated as a meta-optimization problem. Best values for parameters such as sensor location are obtained by optimizing a performance criterion, subject to the constraint posed by the 4D-Var optimization. Tractable computational solutions to this "optimization-constrained" optimization problem are provided. The results of this work can be directly applied to the deployment of intelligent sensors and adaptive observations, as well as to reducing the operating costs of measuring networks, while preserving their ability to capture the essential features of the system under consideration. / Ph. D.

This thesis is concerned with the measurement of physical quantities. Thesis describes sensors in general terms and requirements, which delivers latest trends in the field of automation. The main theme of this thesis is sensors for meassuring pressure, and their connection to the automation chain. In the next part thesis describes the design of the converter for connecting SMART sensors for pressure measuring, that use HART communication protocol, to the automation systems managed by a communication protocol IO-link. First will be described in detail various communication standards by their ISO OSI model, and on this theoretical basis will be based design of converter between communication protocol HART and communication protocol IO-Link. The last part will be Designed software for this converter.